用GPT融合多个强化学习策略，提升脑白质纤维重建精度
TractRLFusion: A GPT-Based Multi-Critic Policy Fusion Framework for Fiber Tractography
- 通过GPT融合多条强化学习路径，实现数据驱动的策略集成
- 在HCP等三个数据集上超越现有方法，纤维连接准确率显著提升
- 适合神经影像分析、精准脑手术规划等临床研究场景
纤维束成像在非侵入性重建脑白质纤维通路中起关键作用，为脑连接研究和精准神经外科规划提供重要信息。尽管传统方法依赖确定性和概率性算法，近年进展得益于监督深度学习（DL）和深度强化学习（DRL），提升了纤维重建效果。但如何在减少伪连接的同时准确重建白质纤维仍是挑战。为此，我们提出TractRLFusion——一种基于GPT的多批评者策略融合框架，通过数据驱动策略融合机制集成多个强化学习策略。该方法采用两阶段训练数据选择流程以实现有效融合，并引入多批评者微调阶段增强鲁棒性与泛化能力。在HCP、ISMRM及TractoInferno数据集上的实验表明，TractRLFusion在准确性和解剖可靠性方面均优于单个强化学习策略以及当前主流经典方法和深度强化学习方法。
原文摘要 · Abstract (English)
Tractography plays a pivotal role in the non-invasive reconstruction of white matter fiber pathways, providing vital information on brain connectivity and supporting precise neurosurgical planning. Although traditional methods relied mainly on classical deterministic and probabilistic approaches, recent progress has benefited from supervised deep learning (DL) and deep reinforcement learning (DRL) to improve tract reconstruction. A persistent challenge in tractography is accurately reconstructing white matter tracts while minimizing spurious connections. To address this, we propose TractRLFusion, a novel GPT-based policy fusion framework that integrates multiple RL policies through a data-driven fusion strategy. Our method employs a two-stage training data selection process for effective policy fusion, followed by a multi-critic fine-tuning phase to enhance robustness and generalization. Experiments on HCP, ISMRM, and TractoInferno datasets demonstrate that TractRLFusion outperforms individual RL policies as well as state-of-the-art classical and DRL methods in accuracy and anatomical reliability.
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